Abstract
An efficient method combining classical (gradient-based) methods for global scalar optimization and genetic algorithms for multiobjective optimization (MOO) is proposed for approximating the Pareto frontier and the Edgeworth–Pareto hull (EPH) of the feasible objective set in complicated nonlinear MOO problems involving piecewise constant functions of criteria with numerous local extrema. An optima injection method is proposed in which the global optima of individual criteria are added to the population of a genetic algorithm. It is experimentally shown that the method is significantly superior to the original genetic algorithm in the order of convergence and the approximation accuracy. Experiments concerning EPH approximation are also performed for the problem of constructing control rules for a cascade of reservoirs with criteria reflecting the reliability with which the requirements imposed on the cascade are met.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Computational Mathematics and Mathematical Physics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
